Overview

Dataset statistics

Number of variables23
Number of observations3312
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory595.2 KiB
Average record size in memory184.0 B

Variable types

Numeric8
Categorical14
DateTime1

Alerts

Country has constant value "United States"Constant
Order Date Year has constant value "2020"Constant
Order ID has a high cardinality: 1687 distinct valuesHigh cardinality
Customer ID has a high cardinality: 693 distinct valuesHigh cardinality
Customer Name has a high cardinality: 693 distinct valuesHigh cardinality
City has a high cardinality: 350 distinct valuesHigh cardinality
Product ID has a high cardinality: 1525 distinct valuesHigh cardinality
Product Name has a high cardinality: 1511 distinct valuesHigh cardinality
Postal Code is highly overall correlated with State and 1 other fieldsHigh correlation
Sales is highly overall correlated with ProfitHigh correlation
Discount is highly overall correlated with Profit and 1 other fieldsHigh correlation
Profit is highly overall correlated with Sales and 2 other fieldsHigh correlation
Profit Margin is highly overall correlated with Discount and 1 other fieldsHigh correlation
State is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Region is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Category is highly overall correlated with Sub-CategoryHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Product ID is uniformly distributedUniform
Product Name is uniformly distributedUniform
Row ID has unique valuesUnique
Discount has 1590 (48.0%) zerosZeros

Reproduction

Analysis started2023-05-03 09:03:43.219042
Analysis finished2023-05-03 09:04:28.153229
Duration44.93 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

Distinct3312
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5087.1075
Minimum13
Maximum9994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:29.084527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile643.55
Q12655.75
median5183.5
Q37498.25
95-th percentile9471.45
Maximum9994
Range9981
Interquartile range (IQR)4842.5

Descriptive statistics

Standard deviation2817.4823
Coefficient of variation (CV)0.5538476
Kurtosis-1.1809217
Mean5087.1075
Median Absolute Deviation (MAD)2409.5
Skewness-0.016172898
Sum16848500
Variance7938206.3
MonotonicityStrictly increasing
2023-05-03T10:04:29.414174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1
 
< 0.1%
6694 1
 
< 0.1%
6671 1
 
< 0.1%
6680 1
 
< 0.1%
6681 1
 
< 0.1%
6682 1
 
< 0.1%
6683 1
 
< 0.1%
6689 1
 
< 0.1%
6690 1
 
< 0.1%
6691 1
 
< 0.1%
Other values (3302) 3302
99.7%
ValueCountFrequency (%)
13 1
< 0.1%
24 1
< 0.1%
35 1
< 0.1%
42 1
< 0.1%
44 1
< 0.1%
72 1
< 0.1%
76 1
< 0.1%
77 1
< 0.1%
78 1
< 0.1%
85 1
< 0.1%
ValueCountFrequency (%)
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%
9989 1
< 0.1%
9988 1
< 0.1%
9982 1
< 0.1%
9970 1
< 0.1%
9969 1
< 0.1%
9968 1
< 0.1%

Order ID
Categorical

Distinct1687
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
CA-2017-100111
 
14
CA-2017-157987
 
12
CA-2017-140949
 
9
CA-2017-117457
 
9
CA-2017-156776
 
8
Other values (1682)
3260 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters46368
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique875 ?
Unique (%)26.4%

Sample

1st rowCA-2017-114412
2nd rowUS-2017-156909
3rd rowCA-2017-107727
4th rowCA-2017-120999
5th rowCA-2017-139619

Common Values

ValueCountFrequency (%)
CA-2017-100111 14
 
0.4%
CA-2017-157987 12
 
0.4%
CA-2017-140949 9
 
0.3%
CA-2017-117457 9
 
0.3%
CA-2017-156776 8
 
0.2%
CA-2017-118017 8
 
0.2%
CA-2017-140872 8
 
0.2%
CA-2017-102925 8
 
0.2%
CA-2017-110905 8
 
0.2%
CA-2017-113278 8
 
0.2%
Other values (1677) 3220
97.2%

Length

2023-05-03T10:04:29.694881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca-2017-100111 14
 
0.4%
ca-2017-157987 12
 
0.4%
ca-2017-140949 9
 
0.3%
ca-2017-117457 9
 
0.3%
ca-2017-110905 8
 
0.2%
us-2017-118087 8
 
0.2%
ca-2017-161956 8
 
0.2%
ca-2017-164756 8
 
0.2%
ca-2017-113278 8
 
0.2%
ca-2017-102925 8
 
0.2%
Other values (1677) 3220
97.2%

Most occurring characters

ValueCountFrequency (%)
1 8448
18.2%
- 6624
14.3%
0 5199
11.2%
2 5169
11.1%
7 4664
10.1%
C 2732
 
5.9%
A 2732
 
5.9%
4 1809
 
3.9%
6 1769
 
3.8%
3 1739
 
3.8%
Other values (5) 5483
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33120
71.4%
Dash Punctuation 6624
 
14.3%
Uppercase Letter 6624
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8448
25.5%
0 5199
15.7%
2 5169
15.6%
7 4664
14.1%
4 1809
 
5.5%
6 1769
 
5.3%
3 1739
 
5.3%
5 1673
 
5.1%
8 1344
 
4.1%
9 1306
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
C 2732
41.2%
A 2732
41.2%
U 580
 
8.8%
S 580
 
8.8%
Dash Punctuation
ValueCountFrequency (%)
- 6624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39744
85.7%
Latin 6624
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8448
21.3%
- 6624
16.7%
0 5199
13.1%
2 5169
13.0%
7 4664
11.7%
4 1809
 
4.6%
6 1769
 
4.5%
3 1739
 
4.4%
5 1673
 
4.2%
8 1344
 
3.4%
Latin
ValueCountFrequency (%)
C 2732
41.2%
A 2732
41.2%
U 580
 
8.8%
S 580
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8448
18.2%
- 6624
14.3%
0 5199
11.2%
2 5169
11.1%
7 4664
10.1%
C 2732
 
5.9%
A 2732
 
5.9%
4 1809
 
3.9%
6 1769
 
3.8%
3 1739
 
3.8%
Other values (5) 5483
11.8%
Distinct322
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Minimum2020-01-01 00:00:00
Maximum2020-12-30 00:00:00
2023-05-03T10:04:29.982718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:30.283194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Standard Class
1897 
Second Class
657 
First Class
572 
Same Day
 
186

Length

Max length14
Median length14
Mean length12.748188
Min length8

Characters and Unicode

Total characters42222
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 1897
57.3%
Second Class 657
 
19.8%
First Class 572
 
17.3%
Same Day 186
 
5.6%

Length

2023-05-03T10:04:30.707537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:31.274037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
class 3126
47.2%
standard 1897
28.6%
second 657
 
9.9%
first 572
 
8.6%
same 186
 
2.8%
day 186
 
2.8%

Most occurring characters

ValueCountFrequency (%)
a 7292
17.3%
s 6824
16.2%
d 4451
10.5%
3312
7.8%
l 3126
7.4%
C 3126
7.4%
S 2740
 
6.5%
n 2554
 
6.0%
r 2469
 
5.8%
t 2469
 
5.8%
Other values (8) 3859
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32286
76.5%
Uppercase Letter 6624
 
15.7%
Space Separator 3312
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7292
22.6%
s 6824
21.1%
d 4451
13.8%
l 3126
9.7%
n 2554
 
7.9%
r 2469
 
7.6%
t 2469
 
7.6%
e 843
 
2.6%
c 657
 
2.0%
o 657
 
2.0%
Other values (3) 944
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
C 3126
47.2%
S 2740
41.4%
F 572
 
8.6%
D 186
 
2.8%
Space Separator
ValueCountFrequency (%)
3312
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38910
92.2%
Common 3312
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7292
18.7%
s 6824
17.5%
d 4451
11.4%
l 3126
8.0%
C 3126
8.0%
S 2740
 
7.0%
n 2554
 
6.6%
r 2469
 
6.3%
t 2469
 
6.3%
e 843
 
2.2%
Other values (7) 3016
7.8%
Common
ValueCountFrequency (%)
3312
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7292
17.3%
s 6824
16.2%
d 4451
10.5%
3312
7.8%
l 3126
7.4%
C 3126
7.4%
S 2740
 
6.5%
n 2554
 
6.0%
r 2469
 
5.8%
t 2469
 
5.8%
Other values (8) 3859
9.1%

Customer ID
Categorical

Distinct693
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
SV-20365
 
20
JL-15835
 
20
Dp-13240
 
19
MH-18115
 
19
LC-16870
 
17
Other values (688)
3217 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters26496
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)2.9%

Sample

1st rowAA-10480
2nd rowSF-20065
3rd rowMA-17560
4th rowLC-16930
5th rowES-14080

Common Values

ValueCountFrequency (%)
SV-20365 20
 
0.6%
JL-15835 20
 
0.6%
Dp-13240 19
 
0.6%
MH-18115 19
 
0.6%
LC-16870 17
 
0.5%
SS-20140 16
 
0.5%
AC-10615 16
 
0.5%
JM-15250 15
 
0.5%
EP-13915 15
 
0.5%
DS-13030 15
 
0.5%
Other values (683) 3140
94.8%

Length

2023-05-03T10:04:31.514226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sv-20365 20
 
0.6%
jl-15835 20
 
0.6%
dp-13240 19
 
0.6%
mh-18115 19
 
0.6%
lc-16870 17
 
0.5%
ss-20140 16
 
0.5%
ac-10615 16
 
0.5%
jm-15250 15
 
0.5%
ep-13915 15
 
0.5%
ds-13030 15
 
0.5%
Other values (683) 3140
94.8%

Most occurring characters

ValueCountFrequency (%)
1 4014
15.1%
- 3312
12.5%
0 2857
 
10.8%
5 2602
 
9.8%
2 1533
 
5.8%
8 998
 
3.8%
3 993
 
3.7%
6 921
 
3.5%
9 890
 
3.4%
4 878
 
3.3%
Other values (29) 7498
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16560
62.5%
Uppercase Letter 6603
 
24.9%
Dash Punctuation 3312
 
12.5%
Lowercase Letter 21
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 610
 
9.2%
C 574
 
8.7%
B 552
 
8.4%
M 528
 
8.0%
D 465
 
7.0%
J 409
 
6.2%
A 385
 
5.8%
H 343
 
5.2%
P 330
 
5.0%
R 299
 
4.5%
Other values (16) 2108
31.9%
Decimal Number
ValueCountFrequency (%)
1 4014
24.2%
0 2857
17.3%
5 2602
15.7%
2 1533
 
9.3%
8 998
 
6.0%
3 993
 
6.0%
6 921
 
5.6%
9 890
 
5.4%
4 878
 
5.3%
7 874
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
p 19
90.5%
l 2
 
9.5%
Dash Punctuation
ValueCountFrequency (%)
- 3312
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19872
75.0%
Latin 6624
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 610
 
9.2%
C 574
 
8.7%
B 552
 
8.3%
M 528
 
8.0%
D 465
 
7.0%
J 409
 
6.2%
A 385
 
5.8%
H 343
 
5.2%
P 330
 
5.0%
R 299
 
4.5%
Other values (18) 2129
32.1%
Common
ValueCountFrequency (%)
1 4014
20.2%
- 3312
16.7%
0 2857
14.4%
5 2602
13.1%
2 1533
 
7.7%
8 998
 
5.0%
3 993
 
5.0%
6 921
 
4.6%
9 890
 
4.5%
4 878
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4014
15.1%
- 3312
12.5%
0 2857
 
10.8%
5 2602
 
9.8%
2 1533
 
5.8%
8 998
 
3.8%
3 993
 
3.7%
6 921
 
3.5%
9 890
 
3.4%
4 878
 
3.3%
Other values (29) 7498
28.3%

Customer Name
Categorical

Distinct693
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Seth Vernon
 
20
John Lee
 
20
Dean percer
 
19
Mick Hernandez
 
19
Lena Cacioppo
 
17
Other values (688)
3217 

Length

Max length22
Median length18
Mean length12.979469
Min length7

Characters and Unicode

Total characters42988
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)2.9%

Sample

1st rowAndrew Allen
2nd rowSandra Flanagan
3rd rowMatt Abelman
4th rowLinda Cazamias
5th rowErin Smith

Common Values

ValueCountFrequency (%)
Seth Vernon 20
 
0.6%
John Lee 20
 
0.6%
Dean percer 19
 
0.6%
Mick Hernandez 19
 
0.6%
Lena Cacioppo 17
 
0.5%
Saphhira Shifley 16
 
0.5%
Ann Chong 16
 
0.5%
Janet Martin 15
 
0.5%
Emily Phan 15
 
0.5%
Darrin Sayre 15
 
0.5%
Other values (683) 3140
94.8%

Length

2023-05-03T10:04:31.824318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
frank 49
 
0.7%
patrick 42
 
0.6%
john 41
 
0.6%
michael 39
 
0.6%
ann 38
 
0.6%
bill 36
 
0.5%
alan 34
 
0.5%
rick 33
 
0.5%
mick 31
 
0.5%
dean 30
 
0.5%
Other values (829) 6281
94.4%

Most occurring characters

ValueCountFrequency (%)
a 3965
 
9.2%
e 3905
 
9.1%
n 3495
 
8.1%
3342
 
7.8%
r 3111
 
7.2%
i 2632
 
6.1%
l 2124
 
4.9%
o 2024
 
4.7%
t 1738
 
4.0%
s 1541
 
3.6%
Other values (46) 15111
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32760
76.2%
Uppercase Letter 6814
 
15.9%
Space Separator 3342
 
7.8%
Other Punctuation 62
 
0.1%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3965
12.1%
e 3905
11.9%
n 3495
10.7%
r 3111
9.5%
i 2632
 
8.0%
l 2124
 
6.5%
o 2024
 
6.2%
t 1738
 
5.3%
s 1541
 
4.7%
h 1306
 
4.0%
Other values (17) 6919
21.1%
Uppercase Letter
ValueCountFrequency (%)
C 620
 
9.1%
S 610
 
9.0%
B 573
 
8.4%
M 550
 
8.1%
D 482
 
7.1%
J 409
 
6.0%
A 400
 
5.9%
H 358
 
5.3%
P 330
 
4.8%
R 310
 
4.5%
Other values (16) 2172
31.9%
Space Separator
ValueCountFrequency (%)
3342
100.0%
Other Punctuation
ValueCountFrequency (%)
' 62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39574
92.1%
Common 3414
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3965
 
10.0%
e 3905
 
9.9%
n 3495
 
8.8%
r 3111
 
7.9%
i 2632
 
6.7%
l 2124
 
5.4%
o 2024
 
5.1%
t 1738
 
4.4%
s 1541
 
3.9%
h 1306
 
3.3%
Other values (43) 13733
34.7%
Common
ValueCountFrequency (%)
3342
97.9%
' 62
 
1.8%
- 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42967
> 99.9%
None 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3965
 
9.2%
e 3905
 
9.1%
n 3495
 
8.1%
3342
 
7.8%
r 3111
 
7.2%
i 2632
 
6.1%
l 2124
 
4.9%
o 2024
 
4.7%
t 1738
 
4.0%
s 1541
 
3.6%
Other values (44) 15090
35.1%
None
ValueCountFrequency (%)
ö 18
85.7%
ü 3
 
14.3%

Segment
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Consumer
1668 
Corporate
980 
Home Office
664 

Length

Max length11
Median length8
Mean length8.897343
Min length8

Characters and Unicode

Total characters29468
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowHome Office
4th rowCorporate
5th rowCorporate

Common Values

ValueCountFrequency (%)
Consumer 1668
50.4%
Corporate 980
29.6%
Home Office 664
 
20.0%

Length

2023-05-03T10:04:32.104668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:32.345596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
consumer 1668
42.0%
corporate 980
24.6%
home 664
 
16.7%
office 664
 
16.7%

Most occurring characters

ValueCountFrequency (%)
o 4292
14.6%
e 3976
13.5%
r 3628
12.3%
C 2648
9.0%
m 2332
7.9%
n 1668
 
5.7%
s 1668
 
5.7%
u 1668
 
5.7%
f 1328
 
4.5%
t 980
 
3.3%
Other values (7) 5280
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24828
84.3%
Uppercase Letter 3976
 
13.5%
Space Separator 664
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4292
17.3%
e 3976
16.0%
r 3628
14.6%
m 2332
9.4%
n 1668
 
6.7%
s 1668
 
6.7%
u 1668
 
6.7%
f 1328
 
5.3%
t 980
 
3.9%
p 980
 
3.9%
Other values (3) 2308
9.3%
Uppercase Letter
ValueCountFrequency (%)
C 2648
66.6%
H 664
 
16.7%
O 664
 
16.7%
Space Separator
ValueCountFrequency (%)
664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28804
97.7%
Common 664
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4292
14.9%
e 3976
13.8%
r 3628
12.6%
C 2648
9.2%
m 2332
8.1%
n 1668
 
5.8%
s 1668
 
5.8%
u 1668
 
5.8%
f 1328
 
4.6%
t 980
 
3.4%
Other values (6) 4616
16.0%
Common
ValueCountFrequency (%)
664
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4292
14.6%
e 3976
13.5%
r 3628
12.3%
C 2648
9.0%
m 2332
7.9%
n 1668
 
5.7%
s 1668
 
5.7%
u 1668
 
5.7%
f 1328
 
4.5%
t 980
 
3.3%
Other values (7) 5280
17.9%

Country
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
United States
3312 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters43056
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 3312
100.0%

Length

2023-05-03T10:04:32.564030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:32.786970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
united 3312
50.0%
states 3312
50.0%

Most occurring characters

ValueCountFrequency (%)
t 9936
23.1%
e 6624
15.4%
U 3312
 
7.7%
n 3312
 
7.7%
i 3312
 
7.7%
d 3312
 
7.7%
3312
 
7.7%
S 3312
 
7.7%
a 3312
 
7.7%
s 3312
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33120
76.9%
Uppercase Letter 6624
 
15.4%
Space Separator 3312
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9936
30.0%
e 6624
20.0%
n 3312
 
10.0%
i 3312
 
10.0%
d 3312
 
10.0%
a 3312
 
10.0%
s 3312
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U 3312
50.0%
S 3312
50.0%
Space Separator
ValueCountFrequency (%)
3312
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39744
92.3%
Common 3312
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9936
25.0%
e 6624
16.7%
U 3312
 
8.3%
n 3312
 
8.3%
i 3312
 
8.3%
d 3312
 
8.3%
S 3312
 
8.3%
a 3312
 
8.3%
s 3312
 
8.3%
Common
ValueCountFrequency (%)
3312
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9936
23.1%
e 6624
15.4%
U 3312
 
7.7%
n 3312
 
7.7%
i 3312
 
7.7%
d 3312
 
7.7%
3312
 
7.7%
S 3312
 
7.7%
a 3312
 
7.7%
s 3312
 
7.7%

City
Categorical

Distinct350
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
New York City
306 
Los Angeles
 
210
San Francisco
 
190
Seattle
 
182
Philadelphia
 
182
Other values (345)
2242 

Length

Max length16
Median length13
Mean length9.3179348
Min length4

Characters and Unicode

Total characters30861
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)2.6%

Sample

1st rowConcord
2nd rowPhiladelphia
3rd rowHouston
4th rowNaperville
5th rowMelbourne

Common Values

ValueCountFrequency (%)
New York City 306
 
9.2%
Los Angeles 210
 
6.3%
San Francisco 190
 
5.7%
Seattle 182
 
5.5%
Philadelphia 182
 
5.5%
Chicago 114
 
3.4%
Houston 104
 
3.1%
Columbus 82
 
2.5%
Dallas 70
 
2.1%
Jacksonville 45
 
1.4%
Other values (340) 1827
55.2%

Length

2023-05-03T10:04:33.013857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 327
 
7.1%
new 314
 
6.8%
york 308
 
6.7%
san 246
 
5.3%
los 210
 
4.5%
angeles 210
 
4.5%
francisco 190
 
4.1%
seattle 182
 
3.9%
philadelphia 182
 
3.9%
chicago 114
 
2.5%
Other values (371) 2340
50.6%

Most occurring characters

ValueCountFrequency (%)
e 2941
 
9.5%
a 2553
 
8.3%
o 2391
 
7.7%
l 2098
 
6.8%
i 2094
 
6.8%
n 1997
 
6.5%
t 1522
 
4.9%
s 1508
 
4.9%
r 1446
 
4.7%
1311
 
4.2%
Other values (40) 11000
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24927
80.8%
Uppercase Letter 4623
 
15.0%
Space Separator 1311
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2941
11.8%
a 2553
10.2%
o 2391
9.6%
l 2098
 
8.4%
i 2094
 
8.4%
n 1997
 
8.0%
t 1522
 
6.1%
s 1508
 
6.0%
r 1446
 
5.8%
c 844
 
3.4%
Other values (15) 5533
22.2%
Uppercase Letter
ValueCountFrequency (%)
C 707
15.3%
S 586
12.7%
L 392
8.5%
N 365
7.9%
A 347
 
7.5%
P 340
 
7.4%
Y 313
 
6.8%
F 311
 
6.7%
D 188
 
4.1%
M 174
 
3.8%
Other values (14) 900
19.5%
Space Separator
ValueCountFrequency (%)
1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29550
95.8%
Common 1311
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2941
 
10.0%
a 2553
 
8.6%
o 2391
 
8.1%
l 2098
 
7.1%
i 2094
 
7.1%
n 1997
 
6.8%
t 1522
 
5.2%
s 1508
 
5.1%
r 1446
 
4.9%
c 844
 
2.9%
Other values (39) 10156
34.4%
Common
ValueCountFrequency (%)
1311
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2941
 
9.5%
a 2553
 
8.3%
o 2391
 
7.7%
l 2098
 
6.8%
i 2094
 
6.8%
n 1997
 
6.5%
t 1522
 
4.9%
s 1508
 
4.9%
r 1446
 
4.7%
1311
 
4.2%
Other values (40) 11000
35.6%

State
Categorical

Distinct47
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
California
663 
New York
352 
Texas
317 
Washington
215 
Pennsylvania
197 
Other values (42)
1568 

Length

Max length20
Median length14
Mean length8.5389493
Min length4

Characters and Unicode

Total characters28281
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowPennsylvania
3rd rowTexas
4th rowIllinois
5th rowFlorida

Common Values

ValueCountFrequency (%)
California 663
20.0%
New York 352
 
10.6%
Texas 317
 
9.6%
Washington 215
 
6.5%
Pennsylvania 197
 
5.9%
Illinois 172
 
5.2%
Ohio 161
 
4.9%
Florida 126
 
3.8%
North Carolina 85
 
2.6%
Tennessee 81
 
2.4%
Other values (37) 943
28.5%

Length

2023-05-03T10:04:33.292795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 663
17.1%
new 424
 
10.9%
york 352
 
9.1%
texas 317
 
8.2%
washington 215
 
5.6%
pennsylvania 197
 
5.1%
illinois 172
 
4.4%
ohio 161
 
4.2%
florida 126
 
3.3%
carolina 93
 
2.4%
Other values (41) 1153
29.8%

Most occurring characters

ValueCountFrequency (%)
a 3522
12.5%
i 3235
11.4%
n 2820
 
10.0%
o 2493
 
8.8%
r 1764
 
6.2%
e 1715
 
6.1%
l 1596
 
5.6%
s 1578
 
5.6%
C 853
 
3.0%
f 665
 
2.4%
Other values (36) 8040
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23849
84.3%
Uppercase Letter 3871
 
13.7%
Space Separator 561
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3522
14.8%
i 3235
13.6%
n 2820
11.8%
o 2493
10.5%
r 1764
7.4%
e 1715
7.2%
l 1596
6.7%
s 1578
6.6%
f 665
 
2.8%
h 658
 
2.8%
Other values (14) 3803
15.9%
Uppercase Letter
ValueCountFrequency (%)
C 853
22.0%
N 532
13.7%
T 398
10.3%
Y 352
9.1%
I 278
 
7.2%
W 252
 
6.5%
M 247
 
6.4%
O 208
 
5.4%
P 197
 
5.1%
F 126
 
3.3%
Other values (11) 428
11.1%
Space Separator
ValueCountFrequency (%)
561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27720
98.0%
Common 561
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3522
12.7%
i 3235
11.7%
n 2820
 
10.2%
o 2493
 
9.0%
r 1764
 
6.4%
e 1715
 
6.2%
l 1596
 
5.8%
s 1578
 
5.7%
C 853
 
3.1%
f 665
 
2.4%
Other values (35) 7479
27.0%
Common
ValueCountFrequency (%)
561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3522
12.5%
i 3235
11.4%
n 2820
 
10.0%
o 2493
 
8.8%
r 1764
 
6.2%
e 1715
 
6.1%
l 1596
 
5.6%
s 1578
 
5.6%
C 853
 
3.0%
f 665
 
2.4%
Other values (36) 8040
28.4%

Postal Code
Real number (ℝ)

Distinct437
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56186.515
Minimum1841
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:33.614869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1841
5-th percentile10009
Q127978.75
median60472.5
Q390032
95-th percentile98103
Maximum99301
Range97460
Interquartile range (IQR)62053.25

Descriptive statistics

Standard deviation31980.376
Coefficient of variation (CV)0.5691824
Kurtosis-1.4592121
Mean56186.515
Median Absolute Deviation (MAD)29576.5
Skewness-0.16255089
Sum1.8608974 × 108
Variance1.0227444 × 109
MonotonicityNot monotonic
2023-05-03T10:04:33.912504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035 89
 
2.7%
10009 81
 
2.4%
10024 73
 
2.2%
98105 71
 
2.1%
94122 70
 
2.1%
94110 67
 
2.0%
10011 63
 
1.9%
98103 63
 
1.9%
94109 53
 
1.6%
19140 52
 
1.6%
Other values (427) 2630
79.4%
ValueCountFrequency (%)
1841 12
0.4%
1852 7
0.2%
2038 2
 
0.1%
2138 2
 
0.1%
2149 9
0.3%
2169 3
 
0.1%
2740 4
 
0.1%
2886 3
 
0.1%
2895 2
 
0.1%
2908 7
0.2%
ValueCountFrequency (%)
99301 2
 
0.1%
99207 4
 
0.1%
98661 1
 
< 0.1%
98632 2
 
0.1%
98502 2
 
0.1%
98226 3
 
0.1%
98208 1
 
< 0.1%
98115 48
1.4%
98105 71
2.1%
98103 63
1.9%

Region
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
West
1095 
East
921 
Central
778 
South
518 

Length

Max length7
Median length4
Mean length4.8611111
Min length4

Characters and Unicode

Total characters16100
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowEast
3rd rowCentral
4th rowCentral
5th rowSouth

Common Values

ValueCountFrequency (%)
West 1095
33.1%
East 921
27.8%
Central 778
23.5%
South 518
15.6%

Length

2023-05-03T10:04:34.201654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:34.456247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
west 1095
33.1%
east 921
27.8%
central 778
23.5%
south 518
15.6%

Most occurring characters

ValueCountFrequency (%)
t 3312
20.6%
s 2016
12.5%
e 1873
11.6%
a 1699
10.6%
W 1095
 
6.8%
E 921
 
5.7%
C 778
 
4.8%
n 778
 
4.8%
r 778
 
4.8%
l 778
 
4.8%
Other values (4) 2072
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12788
79.4%
Uppercase Letter 3312
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3312
25.9%
s 2016
15.8%
e 1873
14.6%
a 1699
13.3%
n 778
 
6.1%
r 778
 
6.1%
l 778
 
6.1%
o 518
 
4.1%
u 518
 
4.1%
h 518
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
W 1095
33.1%
E 921
27.8%
C 778
23.5%
S 518
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 16100
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3312
20.6%
s 2016
12.5%
e 1873
11.6%
a 1699
10.6%
W 1095
 
6.8%
E 921
 
5.7%
C 778
 
4.8%
n 778
 
4.8%
r 778
 
4.8%
l 778
 
4.8%
Other values (4) 2072
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3312
20.6%
s 2016
12.5%
e 1873
11.6%
a 1699
10.6%
W 1095
 
6.8%
E 921
 
5.7%
C 778
 
4.8%
n 778
 
4.8%
r 778
 
4.8%
l 778
 
4.8%
Other values (4) 2072
12.9%

Product ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1525
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
FUR-CH-10003774
 
8
OFF-ST-10001325
 
7
TEC-AC-10003832
 
7
OFF-BI-10004632
 
7
OFF-PA-10003673
 
7
Other values (1520)
3276 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters49680
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique554 ?
Unique (%)16.7%

Sample

1st rowOFF-PA-10002365
2nd rowFUR-CH-10002774
3rd rowOFF-PA-10000249
4th rowTEC-PH-10004093
5th rowOFF-ST-10003282

Common Values

ValueCountFrequency (%)
FUR-CH-10003774 8
 
0.2%
OFF-ST-10001325 7
 
0.2%
TEC-AC-10003832 7
 
0.2%
OFF-BI-10004632 7
 
0.2%
OFF-PA-10003673 7
 
0.2%
OFF-ST-10003208 7
 
0.2%
OFF-PA-10001970 7
 
0.2%
TEC-AC-10004510 7
 
0.2%
OFF-BI-10003274 6
 
0.2%
FUR-TA-10001520 6
 
0.2%
Other values (1515) 3243
97.9%

Length

2023-05-03T10:04:34.788902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fur-ch-10003774 8
 
0.2%
off-st-10003208 7
 
0.2%
tec-ac-10004510 7
 
0.2%
off-pa-10001970 7
 
0.2%
off-st-10001325 7
 
0.2%
off-pa-10003673 7
 
0.2%
tec-ac-10003832 7
 
0.2%
off-bi-10004632 7
 
0.2%
off-bi-10002012 6
 
0.2%
off-st-10000615 6
 
0.2%
Other values (1515) 3243
97.9%

Most occurring characters

ValueCountFrequency (%)
0 11602
23.4%
- 6624
13.3%
F 5070
10.2%
1 5029
10.1%
O 2100
 
4.2%
4 1644
 
3.3%
3 1608
 
3.2%
2 1598
 
3.2%
A 1496
 
3.0%
C 1111
 
2.2%
Other values (17) 11798
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26496
53.3%
Uppercase Letter 16560
33.3%
Dash Punctuation 6624
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5070
30.6%
O 2100
12.7%
A 1496
 
9.0%
C 1111
 
6.7%
U 1061
 
6.4%
T 1016
 
6.1%
R 968
 
5.8%
P 918
 
5.5%
E 695
 
4.2%
B 576
 
3.5%
Other values (6) 1549
 
9.4%
Decimal Number
ValueCountFrequency (%)
0 11602
43.8%
1 5029
19.0%
4 1644
 
6.2%
3 1608
 
6.1%
2 1598
 
6.0%
5 1094
 
4.1%
7 1018
 
3.8%
9 978
 
3.7%
6 976
 
3.7%
8 949
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 6624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33120
66.7%
Latin 16560
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5070
30.6%
O 2100
12.7%
A 1496
 
9.0%
C 1111
 
6.7%
U 1061
 
6.4%
T 1016
 
6.1%
R 968
 
5.8%
P 918
 
5.5%
E 695
 
4.2%
B 576
 
3.5%
Other values (6) 1549
 
9.4%
Common
ValueCountFrequency (%)
0 11602
35.0%
- 6624
20.0%
1 5029
15.2%
4 1644
 
5.0%
3 1608
 
4.9%
2 1598
 
4.8%
5 1094
 
3.3%
7 1018
 
3.1%
9 978
 
3.0%
6 976
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11602
23.4%
- 6624
13.3%
F 5070
10.2%
1 5029
10.1%
O 2100
 
4.2%
4 1644
 
3.3%
3 1608
 
3.2%
2 1598
 
3.2%
A 1496
 
3.0%
C 1111
 
2.2%
Other values (17) 11798
23.7%

Category
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Office Supplies
2002 
Furniture
686 
Technology
624 

Length

Max length15
Median length15
Mean length12.815217
Min length9

Characters and Unicode

Total characters42444
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffice Supplies
2nd rowFurniture
3rd rowOffice Supplies
4th rowTechnology
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 2002
60.4%
Furniture 686
 
20.7%
Technology 624
 
18.8%

Length

2023-05-03T10:04:35.027483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:35.429000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
office 2002
37.7%
supplies 2002
37.7%
furniture 686
 
12.9%
technology 624
 
11.7%

Most occurring characters

ValueCountFrequency (%)
e 5314
12.5%
i 4690
11.0%
p 4004
9.4%
f 4004
9.4%
u 3374
 
7.9%
c 2626
 
6.2%
l 2626
 
6.2%
O 2002
 
4.7%
s 2002
 
4.7%
S 2002
 
4.7%
Other values (10) 9800
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35128
82.8%
Uppercase Letter 5314
 
12.5%
Space Separator 2002
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5314
15.1%
i 4690
13.4%
p 4004
11.4%
f 4004
11.4%
u 3374
9.6%
c 2626
7.5%
l 2626
7.5%
s 2002
 
5.7%
r 1372
 
3.9%
n 1310
 
3.7%
Other values (5) 3806
10.8%
Uppercase Letter
ValueCountFrequency (%)
O 2002
37.7%
S 2002
37.7%
F 686
 
12.9%
T 624
 
11.7%
Space Separator
ValueCountFrequency (%)
2002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40442
95.3%
Common 2002
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5314
13.1%
i 4690
11.6%
p 4004
9.9%
f 4004
9.9%
u 3374
8.3%
c 2626
 
6.5%
l 2626
 
6.5%
O 2002
 
5.0%
s 2002
 
5.0%
S 2002
 
5.0%
Other values (9) 7798
19.3%
Common
ValueCountFrequency (%)
2002
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5314
12.5%
i 4690
11.0%
p 4004
9.4%
f 4004
9.4%
u 3374
 
7.9%
c 2626
 
6.2%
l 2626
 
6.2%
O 2002
 
4.7%
s 2002
 
4.7%
S 2002
 
4.7%
Other values (10) 9800
23.1%

Sub-Category
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Binders
500 
Paper
459 
Furnishings
316 
Phones
294 
Storage
288 
Other values (12)
1455 

Length

Max length11
Median length9
Mean length7.1887077
Min length3

Characters and Unicode

Total characters23809
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaper
2nd rowChairs
3rd rowPaper
4th rowPhones
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 500
15.1%
Paper 459
13.9%
Furnishings 316
9.5%
Phones 294
8.9%
Storage 288
8.7%
Art 282
8.5%
Accessories 275
8.3%
Chairs 190
 
5.7%
Appliances 165
 
5.0%
Labels 114
 
3.4%
Other values (7) 429
13.0%

Length

2023-05-03T10:04:35.662259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 500
15.1%
paper 459
13.9%
furnishings 316
9.5%
phones 294
8.9%
storage 288
8.7%
art 282
8.5%
accessories 275
8.3%
chairs 190
 
5.7%
appliances 165
 
5.0%
labels 114
 
3.4%
Other values (7) 429
13.0%

Most occurring characters

ValueCountFrequency (%)
s 3289
13.8%
e 2934
12.3%
r 2396
 
10.1%
i 1876
 
7.9%
n 1759
 
7.4%
a 1493
 
6.3%
o 1102
 
4.6%
p 1000
 
4.2%
h 833
 
3.5%
c 824
 
3.5%
Other values (18) 6303
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20497
86.1%
Uppercase Letter 3312
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3289
16.0%
e 2934
14.3%
r 2396
11.7%
i 1876
9.2%
n 1759
8.6%
a 1493
7.3%
o 1102
 
5.4%
p 1000
 
4.9%
h 833
 
4.1%
c 824
 
4.0%
Other values (8) 2991
14.6%
Uppercase Letter
ValueCountFrequency (%)
P 753
22.7%
A 722
21.8%
B 576
17.4%
F 380
11.5%
S 347
10.5%
C 212
 
6.4%
L 114
 
3.4%
T 104
 
3.1%
E 71
 
2.1%
M 33
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23809
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3289
13.8%
e 2934
12.3%
r 2396
 
10.1%
i 1876
 
7.9%
n 1759
 
7.4%
a 1493
 
6.3%
o 1102
 
4.6%
p 1000
 
4.2%
h 833
 
3.5%
c 824
 
3.5%
Other values (18) 6303
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3289
13.8%
e 2934
12.3%
r 2396
 
10.1%
i 1876
 
7.9%
n 1759
 
7.4%
a 1493
 
6.3%
o 1102
 
4.6%
p 1000
 
4.2%
h 833
 
3.5%
c 824
 
3.5%
Other values (18) 6303
26.5%

Product Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1511
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Easy-staple paper
 
16
Staples
 
15
Staples in misc. colors
 
12
Staple envelope
 
11
Storex Dura Pro Binders
 
8
Other values (1506)
3250 

Length

Max length127
Median length77
Mean length37.051329
Min length5

Characters and Unicode

Total characters122714
Distinct characters84
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique554 ?
Unique (%)16.7%

Sample

1st rowXerox 1967
2nd rowGlobal Deluxe Stacking Chair, Gray
3rd rowEasy-staple paper
4th rowPanasonic Kx-TS550
5th rowAdvantus 10-Drawer Portable Organizer, Chrome Metal Frame, Smoke Drawers

Common Values

ValueCountFrequency (%)
Easy-staple paper 16
 
0.5%
Staples 15
 
0.5%
Staples in misc. colors 12
 
0.4%
Staple envelope 11
 
0.3%
Storex Dura Pro Binders 8
 
0.2%
Global Wood Trimmed Manager's Task Chair, Khaki 8
 
0.2%
Staple remover 8
 
0.2%
Logitech Desktop MK120 Mouse and keyboard Combo 7
 
0.2%
Adjustable Depth Letter/Legal Cart 7
 
0.2%
Sterilite Officeware Hinged File Box 7
 
0.2%
Other values (1501) 3213
97.0%

Length

2023-05-03T10:04:35.997340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xerox 292
 
1.6%
x 220
 
1.2%
202
 
1.1%
with 190
 
1.0%
for 183
 
1.0%
binders 178
 
1.0%
avery 173
 
0.9%
chair 151
 
0.8%
black 147
 
0.8%
phone 114
 
0.6%
Other values (2479) 16721
90.0%

Most occurring characters

ValueCountFrequency (%)
15134
 
12.3%
e 11264
 
9.2%
r 6984
 
5.7%
o 6626
 
5.4%
a 6392
 
5.2%
i 6207
 
5.1%
l 5439
 
4.4%
n 5044
 
4.1%
s 4940
 
4.0%
t 4800
 
3.9%
Other values (74) 49884
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79386
64.7%
Uppercase Letter 18619
 
15.2%
Space Separator 15275
 
12.4%
Decimal Number 5948
 
4.8%
Other Punctuation 2415
 
2.0%
Dash Punctuation 985
 
0.8%
Final Punctuation 24
 
< 0.1%
Open Punctuation 21
 
< 0.1%
Close Punctuation 21
 
< 0.1%
Math Symbol 9
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11264
14.2%
r 6984
 
8.8%
o 6626
 
8.3%
a 6392
 
8.1%
i 6207
 
7.8%
l 5439
 
6.9%
n 5044
 
6.4%
s 4940
 
6.2%
t 4800
 
6.0%
c 2950
 
3.7%
Other values (18) 18740
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 2071
 
11.1%
C 2026
 
10.9%
B 1851
 
9.9%
P 1616
 
8.7%
M 1037
 
5.6%
D 985
 
5.3%
A 925
 
5.0%
T 878
 
4.7%
F 878
 
4.7%
L 747
 
4.0%
Other values (16) 5605
30.1%
Other Punctuation
ValueCountFrequency (%)
, 1054
43.6%
/ 557
23.1%
" 412
 
17.1%
. 177
 
7.3%
& 90
 
3.7%
' 76
 
3.1%
# 28
 
1.2%
% 12
 
0.5%
! 5
 
0.2%
; 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1216
20.4%
0 977
16.4%
2 769
12.9%
4 568
9.5%
3 513
8.6%
5 477
 
8.0%
8 417
 
7.0%
9 397
 
6.7%
6 318
 
5.3%
7 296
 
5.0%
Space Separator
ValueCountFrequency (%)
15134
99.1%
  141
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 985
100.0%
Final Punctuation
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Initial Punctuation
ValueCountFrequency (%)
8
100.0%
Other Number
ValueCountFrequency (%)
¾ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98005
79.9%
Common 24709
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11264
 
11.5%
r 6984
 
7.1%
o 6626
 
6.8%
a 6392
 
6.5%
i 6207
 
6.3%
l 5439
 
5.5%
n 5044
 
5.1%
s 4940
 
5.0%
t 4800
 
4.9%
c 2950
 
3.0%
Other values (44) 37359
38.1%
Common
ValueCountFrequency (%)
15134
61.2%
1 1216
 
4.9%
, 1054
 
4.3%
- 985
 
4.0%
0 977
 
4.0%
2 769
 
3.1%
4 568
 
2.3%
/ 557
 
2.3%
3 513
 
2.1%
5 477
 
1.9%
Other values (20) 2459
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122531
99.9%
None 151
 
0.1%
Punctuation 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15134
 
12.4%
e 11264
 
9.2%
r 6984
 
5.7%
o 6626
 
5.4%
a 6392
 
5.2%
i 6207
 
5.1%
l 5439
 
4.4%
n 5044
 
4.1%
s 4940
 
4.0%
t 4800
 
3.9%
Other values (68) 49701
40.6%
None
ValueCountFrequency (%)
  141
93.4%
é 6
 
4.0%
¾ 3
 
2.0%
à 1
 
0.7%
Punctuation
ValueCountFrequency (%)
24
75.0%
8
 
25.0%

Sales
Real number (ℝ)

Distinct2623
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.38142
Minimum0.444
Maximum13999.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:36.419069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile5.106
Q117.018
median53.81
Q3205.1057
95-th percentile907.643
Maximum13999.96
Range13999.516
Interquartile range (IQR)188.0877

Descriptive statistics

Standard deviation585.25753
Coefficient of variation (CV)2.6436615
Kurtosis179.3055
Mean221.38142
Median Absolute Deviation (MAD)44.488
Skewness10.554726
Sum733215.26
Variance342526.38
MonotonicityNot monotonic
2023-05-03T10:04:36.740579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 20
 
0.6%
15.552 14
 
0.4%
19.44 13
 
0.4%
20.736 12
 
0.4%
10.368 11
 
0.3%
25.92 9
 
0.3%
32.4 6
 
0.2%
18.24 6
 
0.2%
6.48 5
 
0.2%
8.64 5
 
0.2%
Other values (2613) 3211
97.0%
ValueCountFrequency (%)
0.444 1
< 0.1%
0.556 1
< 0.1%
0.99 1
< 0.1%
1.08 1
< 0.1%
1.188 1
< 0.1%
1.188 2
0.1%
1.248 2
0.1%
1.392 1
< 0.1%
1.408 1
< 0.1%
1.44 1
< 0.1%
ValueCountFrequency (%)
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
7999.98 1
< 0.1%
5443.96 1
< 0.1%
5199.96 1
< 0.1%
5083.96 1
< 0.1%
4799.984 1
< 0.1%
4663.736 1
< 0.1%
4416.174 1
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7669082
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:37.574393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2217761
Coefficient of variation (CV)0.58981424
Kurtosis1.7937415
Mean3.7669082
Median Absolute Deviation (MAD)1
Skewness1.2239691
Sum12476
Variance4.9362891
MonotonicityNot monotonic
2023-05-03T10:04:37.799708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 781
23.6%
3 759
22.9%
5 441
13.3%
4 398
12.0%
1 337
10.2%
7 190
 
5.7%
6 173
 
5.2%
8 99
 
3.0%
9 80
 
2.4%
10 18
 
0.5%
Other values (4) 36
 
1.1%
ValueCountFrequency (%)
1 337
10.2%
2 781
23.6%
3 759
22.9%
4 398
12.0%
5 441
13.3%
6 173
 
5.2%
7 190
 
5.7%
8 99
 
3.0%
9 80
 
2.4%
10 18
 
0.5%
ValueCountFrequency (%)
14 8
 
0.2%
13 8
 
0.2%
12 7
 
0.2%
11 13
 
0.4%
10 18
 
0.5%
9 80
 
2.4%
8 99
 
3.0%
7 190
5.7%
6 173
 
5.2%
5 441
13.3%

Discount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15646739
Minimum0
Maximum0.8
Zeros1590
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:38.024899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20742912
Coefficient of variation (CV)1.3257019
Kurtosis2.4619831
Mean0.15646739
Median Absolute Deviation (MAD)0.2
Skewness1.6998317
Sum518.22
Variance0.04302684
MonotonicityNot monotonic
2023-05-03T10:04:38.264286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1590
48.0%
0.2 1223
36.9%
0.7 138
 
4.2%
0.8 107
 
3.2%
0.4 69
 
2.1%
0.3 68
 
2.1%
0.6 39
 
1.2%
0.1 28
 
0.8%
0.5 19
 
0.6%
0.15 16
 
0.5%
Other values (2) 15
 
0.5%
ValueCountFrequency (%)
0 1590
48.0%
0.1 28
 
0.8%
0.15 16
 
0.5%
0.2 1223
36.9%
0.3 68
 
2.1%
0.32 11
 
0.3%
0.4 69
 
2.1%
0.45 4
 
0.1%
0.5 19
 
0.6%
0.6 39
 
1.2%
ValueCountFrequency (%)
0.8 107
 
3.2%
0.7 138
 
4.2%
0.6 39
 
1.2%
0.5 19
 
0.6%
0.45 4
 
0.1%
0.4 69
 
2.1%
0.32 11
 
0.3%
0.3 68
 
2.1%
0.2 1223
36.9%
0.15 16
 
0.5%

Profit
Real number (ℝ)

Distinct2913
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.21234
Minimum-3839.9904
Maximum6719.9808
Zeros19
Zeros (%)0.6%
Negative620
Negative (%)18.7%
Memory size26.0 KiB
2023-05-03T10:04:38.611645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3839.9904
5-th percentile-52.39104
Q11.7632
median8.2968
Q328.315125
95-th percentile163.80095
Maximum6719.9808
Range10559.971
Interquartile range (IQR)26.551925

Descriptive statistics

Standard deviation241.86434
Coefficient of variation (CV)8.5729983
Kurtosis300.45368
Mean28.21234
Median Absolute Deviation (MAD)10.7688
Skewness8.2171767
Sum93439.27
Variance58498.36
MonotonicityNot monotonic
2023-05-03T10:04:38.899680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
0.6%
6.2208 16
 
0.5%
9.3312 13
 
0.4%
7.2576 12
 
0.4%
5.4432 11
 
0.3%
3.6288 9
 
0.3%
12.4416 7
 
0.2%
15.552 6
 
0.2%
114.9385 4
 
0.1%
9.072 4
 
0.1%
Other values (2903) 3211
97.0%
ValueCountFrequency (%)
-3839.9904 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2287.782 1
< 0.1%
-1306.5504 1
< 0.1%
-1237.8462 1
< 0.1%
-1143.891 1
< 0.1%
-1141.47 1
< 0.1%
-1049.3406 1
< 0.1%
-1002.7836 1
< 0.1%
ValueCountFrequency (%)
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
3919.9888 1
< 0.1%
2504.2216 1
< 0.1%
1906.485 1
< 0.1%
1668.205 1
< 0.1%
1453.1238 1
< 0.1%
1439.976 1
< 0.1%
1379.977 1
< 0.1%
1351.9896 1
< 0.1%

Order Date Year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2020
3312 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13248
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 3312
100.0%

Length

2023-05-03T10:04:39.130028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-03T10:04:40.147656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2020 3312
100.0%

Most occurring characters

ValueCountFrequency (%)
2 6624
50.0%
0 6624
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13248
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6624
50.0%
0 6624
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13248
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6624
50.0%
0 6624
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6624
50.0%
0 6624
50.0%

Order Date Month
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7309783
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-05-03T10:04:40.337335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3412255
Coefficient of variation (CV)0.43218664
Kurtosis-1.0050948
Mean7.7309783
Median Absolute Deviation (MAD)2
Skewness-0.42971334
Sum25605
Variance11.163788
MonotonicityNot monotonic
2023-05-03T10:04:40.536952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 462
13.9%
9 459
13.9%
11 459
13.9%
10 298
9.0%
6 245
7.4%
5 242
7.3%
3 238
7.2%
7 226
6.8%
8 218
6.6%
4 203
6.1%
Other values (2) 262
7.9%
ValueCountFrequency (%)
1 155
 
4.7%
2 107
 
3.2%
3 238
7.2%
4 203
6.1%
5 242
7.3%
6 245
7.4%
7 226
6.8%
8 218
6.6%
9 459
13.9%
10 298
9.0%
ValueCountFrequency (%)
12 462
13.9%
11 459
13.9%
10 298
9.0%
9 459
13.9%
8 218
6.6%
7 226
6.8%
6 245
7.4%
5 242
7.3%
4 203
6.1%
3 238
7.2%

Profit Margin
Real number (ℝ)

Distinct797
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11598128
Minimum-2.75
Maximum0.5
Zeros19
Zeros (%)0.6%
Negative620
Negative (%)18.7%
Memory size26.0 KiB
2023-05-03T10:04:40.868510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.75
5-th percentile-0.8
Q10.075
median0.27
Q30.3625
95-th percentile0.48
Maximum0.5
Range3.25
Interquartile range (IQR)0.2875

Descriptive statistics

Standard deviation0.48126539
Coefficient of variation (CV)4.1495091
Kurtosis10.442779
Mean0.11598128
Median Absolute Deviation (MAD)0.16
Skewness-2.9579568
Sum384.13001
Variance0.23161637
MonotonicityNot monotonic
2023-05-03T10:04:41.166833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.48 74
 
2.2%
0.48 71
 
2.1%
0.35 69
 
2.1%
0.26 68
 
2.1%
0.49 61
 
1.8%
0.47 55
 
1.7%
0.46 52
 
1.6%
0.5 52
 
1.6%
0.35 51
 
1.5%
0.27 44
 
1.3%
Other values (787) 2715
82.0%
ValueCountFrequency (%)
-2.75 2
0.1%
-2.7 2
0.1%
-2.7 1
 
< 0.1%
-2.7 3
0.1%
-2.65 2
0.1%
-2.6 2
0.1%
-2.6 1
 
< 0.1%
-2.55 2
0.1%
-2.55 3
0.1%
-2.55 2
0.1%
ValueCountFrequency (%)
0.5 52
1.6%
0.49 5
 
0.2%
0.49 30
0.9%
0.49 61
1.8%
0.49 9
 
0.3%
0.48 5
 
0.2%
0.48 71
2.1%
0.48 74
2.2%
0.48 21
 
0.6%
0.48 3
 
0.1%

Interactions

2023-05-03T10:04:19.983274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:51.670719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:54.559674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:57.081779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:59.969658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:02.722879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:05.935822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:11.175356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:20.623468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:52.026741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:54.881848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:57.427705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:00.323715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:03.022836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:06.740471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:13.183339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:21.130247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:52.367606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:55.191338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:57.716283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:00.639732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:03.583032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:07.180361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:14.149320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:21.529074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:52.723640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:55.524284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:58.064969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:00.964179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:03.924300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:07.579822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:14.884912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:22.144576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:53.039940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:55.814412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:58.459362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:01.302355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:04.377603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:08.378577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:15.346288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:22.510774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:53.413255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:56.187487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:58.814039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:01.691662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:04.737346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:09.301959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:15.857666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:22.833325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:53.919808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:56.468105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:59.224927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:02.008601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:05.135286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:09.845596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:16.370822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:23.224775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:54.239233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:56.769055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:03:59.542212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:02.387071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:05.475268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:10.492356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T10:04:17.047203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-03T10:04:41.415677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Row IDPostal CodeSalesQuantityDiscountProfitOrder Date MonthProfit MarginShip ModeSegmentStateRegionCategorySub-Category
Row ID1.0000.0070.0130.0100.029-0.028-0.053-0.0410.0650.1010.1590.0710.0240.000
Postal Code0.0071.0000.0060.0260.0210.017-0.026-0.0070.0500.0660.9590.9390.0320.022
Sales0.0130.0061.0000.317-0.0710.5170.005-0.1900.0400.0220.0490.0000.0830.215
Quantity0.0100.0260.3171.000-0.0060.2420.0150.0310.0070.0140.0000.0000.0450.034
Discount0.0290.021-0.071-0.0061.000-0.5530.001-0.6380.0450.0000.3480.2920.3710.349
Profit-0.0280.0170.5170.242-0.5531.0000.0010.5120.0210.0000.0240.0120.0640.165
Order Date Month-0.053-0.0260.0050.0150.0010.0011.0000.0150.0910.0740.1640.0680.0220.015
Profit Margin-0.041-0.007-0.1900.031-0.6380.5120.0151.0000.0090.0000.2140.2020.2640.307
Ship Mode0.0650.0500.0400.0070.0450.0210.0910.0091.0000.0310.1720.0230.0000.000
Segment0.1010.0660.0220.0140.0000.0000.0740.0000.0311.0000.1610.0180.0000.053
State0.1590.9590.0490.0000.3480.0240.1640.2140.1720.1611.0000.9930.0000.016
Region0.0710.9390.0000.0000.2920.0120.0680.2020.0230.0180.9931.0000.0150.000
Category0.0240.0320.0830.0450.3710.0640.0220.2640.0000.0000.0000.0151.0000.998
Sub-Category0.0000.0220.2150.0340.3490.1650.0150.3070.0000.0530.0160.0000.9981.000

Missing values

2023-05-03T10:04:24.196875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-03T10:04:26.789539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfitOrder Date YearOrder Date MonthProfit Margin
013CA-2017-1144122020-04-15Standard ClassAA-10480Andrew AllenConsumerUnited StatesConcordNorth Carolina28027SouthOFF-PA-10002365Office SuppliesPaperXerox 196715.55230.25.4432202040.350000
124US-2017-1569092020-07-16Second ClassSF-20065Sandra FlanaganConsumerUnited StatesPhiladelphiaPennsylvania19140EastFUR-CH-10002774FurnitureChairsGlobal Deluxe Stacking Chair, Gray71.37220.3-1.019620207-0.014286
235CA-2017-1077272020-10-19Second ClassMA-17560Matt AbelmanHome OfficeUnited StatesHoustonTexas77095CentralOFF-PA-10000249Office SuppliesPaperEasy-staple paper29.47230.29.94682020100.337500
342CA-2017-1209992020-09-10Standard ClassLC-16930Linda CazamiasCorporateUnited StatesNapervilleIllinois60540CentralTEC-PH-10004093TechnologyPhonesPanasonic Kx-TS550147.16840.216.5564202090.112500
444CA-2017-1396192020-09-19Standard ClassES-14080Erin SmithCorporateUnited StatesMelbourneFlorida32935SouthOFF-ST-10003282Office SuppliesStorageAdvantus 10-Drawer Portable Organizer, Chrome Metal Frame, Smoke Drawers95.61620.29.5616202090.100000
572CA-2017-1144402020-09-14Second ClassTB-21520Tracy BlumsteinConsumerUnited StatesJacksonMichigan49201CentralOFF-PA-10004675Office SuppliesPaperTelephone Message Books with Fax/Mobile Section, 5 1/2" x 3 3/16"19.05030.08.7630202090.460000
676US-2017-1180382020-12-09First ClassKB-16600Ken BrennanCorporateUnited StatesHoustonTexas77041CentralOFF-BI-10004182Office SuppliesBindersEconomy Binders1.24830.8-1.9344202012-1.550000
777US-2017-1180382020-12-09First ClassKB-16600Ken BrennanCorporateUnited StatesHoustonTexas77041CentralFUR-FU-10000260FurnitureFurnishings6" Cubicle Wall Clock, Black9.70830.6-5.8248202012-0.600000
878US-2017-1180382020-12-09First ClassKB-16600Ken BrennanCorporateUnited StatesHoustonTexas77041CentralOFF-ST-10000615Office SuppliesStorageSimpliFile Personal File, Black Granite, 15w x 6-15/16d x 11-1/4h27.24030.22.72402020120.100000
985US-2017-1196622020-11-13First ClassCS-12400Christopher SchildHome OfficeUnited StatesChicagoIllinois60623CentralOFF-ST-10003656Office SuppliesStorageSafco Industrial Wire Shelving230.37630.2-48.9549202011-0.212500
Row IDOrder IDOrder DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfitOrder Date YearOrder Date MonthProfit Margin
33029968CA-2017-1538712020-12-11Standard ClassRB-19435Richard BiernerConsumerUnited StatesPlainfieldNew Jersey7060EastOFF-BI-10004209Office SuppliesBindersFellowes Twister Kit, Gray/Clear, 3/pkg40.20050.018.09002020120.450
33039969CA-2017-1538712020-12-11Standard ClassRB-19435Richard BiernerConsumerUnited StatesPlainfieldNew Jersey7060EastOFF-BI-10004600Office SuppliesBindersIbico Ibimaster 300 Manual Binding System735.98020.0331.19102020120.450
33049970CA-2017-1538712020-12-11Standard ClassRB-19435Richard BiernerConsumerUnited StatesPlainfieldNew Jersey7060EastOFF-AP-10003622Office SuppliesAppliancesBravo II Megaboss 12-Amp Hard Body Upright, Replacement Belts, 2 Belts per Pack22.75070.06.59752020120.290
33059982CA-2017-1635662020-08-03First ClassTB-21055Ted ButterfieldConsumerUnited StatesFairfieldOhio45014EastOFF-LA-10004484Office SuppliesLabelsAvery 47616.52050.25.3690202080.325
33069988CA-2017-1636292020-11-17Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthTEC-AC-10001539TechnologyAccessoriesLogitech G430 Surround Sound Gaming Headset with Dolby 7.1 Technology79.99010.028.79642020110.360
33079989CA-2017-1636292020-11-17Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthTEC-PH-10004006TechnologyPhonesPanasonic KX - TS880B Telephone206.10050.055.64702020110.270
33089991CA-2017-1212582020-02-26Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestFUR-FU-10000747FurnitureFurnishingsTenex B1-RE Series Chair Mats for Low Pile Carpets91.96020.015.6332202020.170
33099992CA-2017-1212582020-02-26Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestTEC-PH-10003645TechnologyPhonesAastra 57i VoIP phone258.57620.219.3932202020.075
33109993CA-2017-1212582020-02-26Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestOFF-PA-10004041Office SuppliesPaperIt's Hot Message Books with Stickers, 2 3/4" x 5"29.60040.013.3200202020.450
33119994CA-2017-1199142020-05-04Second ClassCC-12220Chris CortesConsumerUnited StatesWestminsterCalifornia92683WestOFF-AP-10002684Office SuppliesAppliancesAcco 7-Outlet Masterpiece Power Center, Wihtout Fax/Phone Line Protection243.16020.072.9480202050.300